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Record W3180995513 · doi:10.3324/haematol.2021.278762

The RUNX1 database (RUNX1db): establishment of an expert curated RUNX1 registry and genomics database as a public resource for familial platelet disorder with myeloid malignancy

2021· article· en· W3180995513 on OpenAlexafffund
Claire C. Homan, Sarah L. King‐Smith, David Lawrence, Peer Arts, Jinghua Feng, James Andrews, M. Armstrong, Thuong Ha, Julia Dobbins, Michael W. Drazer, Kai Yu, Csaba Bödör, Alan Cantor, Mario Cazzola, Erin Degelman, Courtney D. DiNardo, Nicolas Duployez, Rémi Favier, Stefan Fröhling, Jude Fitzgibbon, Jeffery M. Klco, Alwin Krämer, Mineo Kurokawa, Joanne Lee, Luca Malcovati, Neil V. Morgan, Georges Natsoulis, Carolyn Owen, Keyur P. Patel, Claude Preudhomme, Hana Raslová, Hugh Young Rienhoff, Tim Ripperger, Rachael Schulte, Kiran Tawana, Elvira Deolinda Rodrigues Pereira Velloso, Benedict Yan, Paul Liu, Lucy A. Godley, Andreas Schreiber, Christopher N Hahn, Hamish S. Scott, Anna Brown

Bibliographic record

VenueHaematologica · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsFoothills Medical Centre
FundersNHLBI Division of Intramural ResearchMedical Research CouncilUniversity of Texas MD Anderson Cancer CenterDirectorate for Biological SciencesNational Institutes of HealthDeutschen Konsortium für Translationale KrebsforschungNational University Health SystemMasarykova UniverzitaCanterbury District Health BoardNational Health and Medical Research CouncilUniversidade de São PauloMedizinischen Hochschule HannoverGovernment of South AustraliaVanderbilt UniversityUniversity of ChicagoUniversità degli Studi di PaviaSociedade Beneficente Israelita Brasileira Albert EinsteinUniversity of TokyoInstitut Gustave-RoussySemmelweis EgyetemEuropean Hematology AssociationErasmus Medisch CentrumUniversity of South AustraliaInstitut National de la Santé et de la Recherche MédicaleCancer Research UKVanderbilt University Medical CenterQueen Mary University of LondonHospital Research FoundationAssociazione Italiana per la Ricerca sul CancroNational Cancer InstituteUniversität HeidelbergAustralian Cancer Research FoundationCancer Council South AustraliaUniversité de MontréalLigue Contre le CancerDeutsches KrebsforschungszentrumRoyal Adelaide HospitalFondazione IRCCS Policlinico San MatteoBundesministerium für Bildung und ForschungAssistance publique-Hôpitaux de ParisMassachusetts General Hospital
KeywordsDatabaseRUNX1GenomicsMedicineBiologyComputer scienceGeneticsGeneGenomeTranscription factor

Abstract

fetched live from OpenAlex

The RUNX1 database (RUNX1db): establishment of an expert curated RUNX1 registry and genomics database as a public resource for familial platelet disorder with myeloid malignancy

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.022
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0090.009
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0040.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0220.014

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.300
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations55
Published2021
Admission routes2
Has abstractyes

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